class_docstr: Adam - A Method for Stochastic Optimization.
  
  Attributes
  ----------
  lr: float
      The learning rate that controls learning step size. Adjustable in progress, default: 0.01.
  batch: int
      The mini-batch size, number of examples used to compute single gradient step, default: 10.
  beta1: float
      Controls the exponential decay rate for the first moment estimates, default: 0.9.
  beta2: float
      Controls the exponential decay rate for the second moment estimates, default: 0.999.
  eps: float
      The epsilon, a very small number to prevent any division by zero in the implementation, default: 1e-8.
  
  Methods
  -------
  set_lr(value, min, max)
      Set value for learning rate.
  set_batch(value, allow_set)
      Set value for batch size.
  set_beta1(value, min, max)
      Set value for beta1.
  set_beta2(value, min, max)
      Set value for beta2.
  set_eps(value, min, max)
      Set value for epsilon.
code: def beta1(self):
      return self._beta1
  
label_desc: Use image node_id 9 for calling the AdamParams obj's underlying member method code with example usage: obj.beta1() and returns: self